This file describes the files and variables used for replicating "All Sins are not Created Equal: The Factors that Drive Perceptions of Corruption Severity".

LIST AND DESCRIPTION OF FILES:

Conjoint_experiment_raw.dta:  Raw survey data 

respondent_level_clean.dta: A cleaned dataset where the unit of observation is the individual respondent. This dataset is created from Conjoint_experiment_raw.dta using JEPS_replication.do.

profile_level_clean.dta:  A cleaned dataset where the unit of observation is a single profile within a profile pair for the conjoint experiment. As each respondent saw 4 profile pairs, this means there are 8 observations per respondent in this dataset. This dataset is created from Conjoint_experiment_raw.dta using JEPS_replication.do.

JEPS_replication.do: A Stata .do file that imports Conjoint_experiment_raw.dta, creates respondent_level_clean.dta and profile_level_clean.dta, then uses these files to create the tables and figures used in the paper�s main text and the online appendix.

Martin_JEPS_Protocols.pdf: Reports the full text of the conjoint experiment, including the icons used to visually represent each attribute-level. 

Martin_Replication_Log.smcl: a log file showing the output for JEPS_replication.do.

DESCRIPTION OF VARIABLES USED IN ANALYSIS

age: self-reported age in years

att1:  categorical variable listing the treatment assignment for attribute 1 (whether the official was elected or appointed) for each profile

att2:  categorical variable listing the treatment assignment for attribute 2 (whether the official was part of local or central government) for each profile

att3:  categorical variable listing the treatment assignment for attribute 3 (whether the official stole local taxes, foreign aid, or central transfers) for each profile

att4: categorical variable listing the treatment assignment for attribute 4 (whether the official spent stolen funds on himself & his family; kin and village; or buying election support) for each profile

att5: categorical variable listing the treatment assignment for attribute 5 (whether the stolen funds were supposed to be spent on bureaucrat�s salaries; water; infrastructure/roads; education; or health care) for each profile.

avg_action:  variable that reports the average self-reported willingness to take each of the 4 possible actions in response to rumors of corruption (goprotest, contact_official, campaign_against, and talkneighb). Each action is measured on a 4-point scale where 4 is �very likely� and 1 is �very unlikely�. 

avg_rank:  a respondent-level variable that takes the average of the 5-point severity ranking variable for each of the 8 profiles respondents saw in the conjoint experiment

boda:  a dummy variable that takes a value of 1 if a respondent was a motorcycle taxi driver (boda-boda)

buyel:  a binary variable that takes a value of 1 if, for a given conjoint profile, the corrupt official spent the stolen funds on buying election support for his party

campaign_against:  4-point variable measuring self-reported likelihood of campaigning against an official accused of corruption in the next election. 1=very unlikely; 2=somewhat unlikely; 3=somewhat likely; 4=very likely.  

chosen:  Binary variable that takes a value of 1 if an individual profile within a profile pair was selected as more deserving of punishment

contact_official: 4-point variable measuring self-reported likelihood of complaining to a local official in response to rumors that a local politician is corrupt. 1=very unlikely; 2=somewhat unlikely; 3=somewhat likely; 4=very likely.  

e_1 � e_10: dummy variables for each enumerator

educ: a binary variable that takes a value of 1 if, for a given conjoint profile, the money was supposed to be spent on education

education_years: self-reported years of education completed. Zero indicates no education; values between 1 and 7 indicate 1-7 years of primary school; values between 8 and 13 indicate finishing primary school and completing 1- 6 years of secondary school;  values of 14 indicate completing trade school; some university; a university degree; or a certificate course

elect:  a binary variable that takes a value of 1 if, for a given conjoint profile, the corrupt official was elected (rather than appointed)

freefair: A binary variable that takes a value of 1 if a respondent reported that the last presidential election was free and fair, and 0 otherwise

goprotest: 4-point variable measuring self-reported likelihood of going to a protest in response to rumors that a local politician is corrupt. 1=very unlikely; 2=somewhat unlikely; 3=somewhat likely; 4=very likely. 

health: a binary variable that takes a value of 1 if, for a given conjoint profile, the money was supposed to be spent on health care

infra: a binary variable that takes a value of 1 if, for a given conjoint profile, the money was supposed to be spent on infrastructure 

kinvill: a binary variable that takes a value of 1 if, for a given conjoint profile, the corrupt official spent the stolen funds on helping his kin and village networks

local: a binary variable that takes a value of 1 if, for a given conjoint profile, the corrupt official was part of local (rather than central) government, and 0 otherwise

male: a binary variable that takes a value of 1 if the respondent was male

market: a dummy variable that takes a value of 1 if a respondent was a market vendor

pid: a unique identifier for each respondent

polparty: A binary variable that takes a value of 1 if a respondent reported belonging to any political party, and 0 otherwise

postsec_ed: A binary variable that takes a value of 1 if the respondent�s highest reported level of education was at least some post-secondary education

presvote: a binary variable that takes a value of 1 if the respondent reported voting in the last presidential election

profile: reports whether a single profile in the conjoint experiment was part of the 1st, 2nd, 3rd, or 4th pair a respondent saw

profileab: reports whether a single profile in the conjoint experiment appeared as the first or second profile a respondent saw within a given profile pair

rank: A 5-point categorical variable that takes a value of 1-5 depending on respondent�s perception of how severe each profile�s crime was (5=�most severe�)

reg_dum: a binary variable that takes a value of 1 if the respondent reported being registered to vote in the last election

shopkeeper: a dummy variable that takes a value of 1 if a respondent was a shopkeeper

steal_upset: 5-point variable measuring how upset respondent said they would be in response to rumors that a local official was corrupt. 1= not upset at all; 2=a little upset; 3=somewhat upset; 4=very upset; 5=extremely upset.

talkneighb: 4-point variable measuring self-reported likelihood of talking to neighbors about rumors that a local politician is corrupt. 1=very unlikely; 2=somewhat unlikely; 3=somewhat likely; 4=very likely.  

tax: a binary variable that takes a value of 1 if, for a given conjoint profile, the corrupt official stole funds that came from local taxes

town_2: String variable reporting town in which enumeration was conducted (cleaned & corrected)

townfe_1 � townfe_18: dummy variables for each town in which enumeration conducted

transfer: a binary variable that takes a value of 1 if, for a given conjoint profile, the corrupt official stole funds that came from central transfers

urban_dum: A binary variable that takes a 1 if a respondent reported living in a town, 0 if they reported living in a village

water: a binary variable that takes a value of 1 if, for a given conjoint profile, the money was supposed to be spent on water and sanitation

weekly_profit: estimated weekly profits for respondent. Created by multiplying together the number of days per week each respondent reported working, and the self-reported profits from a typical day of work 

